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All Journal Formosa Journal of Science and Technology (FJST)
Putra, Bagas Mahendra
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Humanities Computer Science & IT Education Industrial & Manufacturing Engineering Social Sciences

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Journal of Lupus Disease Classification Study Using Naïve Bayes Method Desyani, Teti; Putra, Bagas Mahendra; Haura, Al; Juanda, La; Ainun, Vivi; Rosyani, Perani
Formosa Journal of Science and Technology Vol. 4 No. 1 (2025): January 2025
Publisher : PT FORMOSA CENDEKIA GLOBAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55927/fjst.v4i1.13489

Abstract

The chronic autoimmune illness known as systemic lupus erythematosus (SLE) is typified by tissue destruction in multiple organs and systemic inflammation. Diagnosing this condition might be difficult because of its varied and fluctuating clinical symptoms. The goal of this research is to use clinical and laboratory data to create a classification model for SLE diagnosis using the Naïve Bayes approach. Age, gender, clinical symptoms, and the outcomes of laboratory tests are among the information gathered for this study. This approach is crucial for helping with SLE management and early diagnosis. The Naïve Bayes model was used to assess and categorize these data according to the severity of the condition. The accuracy, precision, and recall measures were used in the study to evaluate the Naïve Bayes model. The outcomes demonstrated how well the Naïve Bayes algorithm can categorize SLE patients.
Use of Expert System in Determining Water Management in Agriculture with Methods: Fuzzy Logic and Rule-Based Decision Making Saprudin, Saprudin; Rosyani, Perani; Putra, Bagas Mahendra; Haura, Al; Juanda, La; Ainun, Vivi
Formosa Journal of Science and Technology Vol. 4 No. 1 (2025): January 2025
Publisher : PT FORMOSA CENDEKIA GLOBAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55927/fjst.v4i1.13490

Abstract

This project aims to develop an expert system to support agricultural water management using Fuzzy Logic and Rule-Based Decision Making methods. This system is important for improving agricultural yields and environmental sustainability, especially with the increasing demand for food and the impacts of climate change. Data was taken from Kaggle, including information on soil conditions, temperature, and rainfall. Data processing includes missing value removal, outlier detection, and splitting the data into 80% training and 20% testing. Fuzzy Logic was chosen because it is able to handle data uncertainty and provide accurate output regarding crop water requirements, while Rule-Based Decision Making utilizes expert knowledge-based rules for decision making. Simulation results show that the Fuzzy Logic model provides recommendations for water needs according to actual conditions, with high responsiveness to soil moisture and temperature. The system is expected to be a tool to assist farmers in decision-making, increase agricultural productivity, and support water sustainability. This research contributes to the development of expert systems in agriculture and natural resource management based on modern technology.
Co-Authors Ainun, Vivi Haura, Al Juanda, La Perani Rosyani Saprudin Saprudin, Saprudin Teti Desyani